{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Training labels shape: ', y_train.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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cAP/o5372in38ez/9122g89BoNSCT8Qs8B9eVdWGq82z25T0Xzl8gTqU/g/Y+ro59vVrm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\n3KCj0Wrt3XYRseUkKbkX0l5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gYNjdFqbVmoRb75XIyUHY48kXngKgGyZkuR9BxnDRWobM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\nMs5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VfCIZn6GSUMZM24tRMI9FOQ4itgd48vgIKb+gHX8Ch5UHjvIQcMBvtVogLE3YmlXL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Qs3Njfw91gYM75ziO++8S4A4DMvv2A67ACJFtNcqtR4+JAYim+98T28/YALuG1gi7XJmqWG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fN3/6e/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\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wwgUKRX3l1euIuvTc7915iNuPyJN2r5xjY0Z27JWgjXMsE6BNidTSRbdffwWrXHmQTE/R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hcHJqNjGWRNBUpyWzBTrb5jNb9RbUhXkm7W4Ld/fM9+8/2kXpm0kx7U9Qo264WquGkoQ0Z4sc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a1742ad10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-395120c9db7c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Test your implementation:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdists\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompute_distances_two_loops\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mdists\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/htan/code/cs231n/assignment1/cs231n/classifiers/k_nearest_neighbor.py\u001b[0m in \u001b[0;36mcompute_distances_two_loops\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m     63\u001b[0m         \u001b[1;31m#####################################################################\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m         \u001b[1;31m#dists[i,j] = np.sqrt(np.sum(np.square(X[i,:]-self.X_train[j,:])))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m         \u001b[0mdists\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdists\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/htan/apps/anaconda2/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36mnorm\u001b[1;34m(x, ord, axis, keepdims)\u001b[0m\n\u001b[0;32m   2121\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2122\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'K'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2123\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2124\u001b[0m                 \u001b[0msqnorm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2125\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/home/htan/apps/anaconda2/lib/python2.7/site-packages/numpy/linalg/linalg.pyc\u001b[0m in \u001b[0;36misComplexType\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0misComplexType\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 112\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0missubclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomplexfloating\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m _real_types_map = {single : single,\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print dists.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dists' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-8eaa0093fd38>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# We can visualize the distance matrix: each row is a single test example and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# its distances to training examples\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdists\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'none'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dists' is not defined"
     ]
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  - The test case is different from most of the training data.\n",
    "  - The particular item in the training set is different from most of the test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dists' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-97d0a736a796>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0my_test_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict_labels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdists\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mnum_correct\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_test_pred\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0maccuracy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_correct\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mnum_test\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m'Got %d / %d correct => accuracy: %f'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnum_correct\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnum_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'dists' is not defined"
     ]
    }
   ],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')\n",
    "print 'Difference was: %f' % (difference, )\n",
    "if difference < 0.001:\n",
    "  print 'Good! The distance matrices are the same'\n",
    "else:\n",
    "  print 'Uh-oh! The distance matrices are different'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print 'Difference was: %f' % (difference, )\n",
    "if difference < 0.001:\n",
    "  print 'Good! The distance matrices are the same'\n",
    "else:\n",
    "  print 'Uh-oh! The distance matrices are different'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 48.659934 seconds\n",
      "One loop version took 77.432321 seconds\n",
      "No loop version took 0.458865 seconds\n"
     ]
    }
   ],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "  \"\"\"\n",
    "  Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "  \"\"\"\n",
    "  import time\n",
    "  tic = time.time()\n",
    "  f(*args)\n",
    "  toc = time.time()\n",
    "  return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print 'Two loop version took %f seconds' % two_loop_time\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print 'One loop version took %f seconds' % one_loop_time\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print 'No loop version took %f seconds' % no_loop_time\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluating k=1\n",
      "0.526\n",
      "0.514\n",
      "0.528\n",
      "0.556\n",
      "0.532\n",
      "evaluating k=3\n",
      "0.482\n",
      "0.498\n",
      "0.486\n",
      "0.546\n",
      "0.528\n",
      "evaluating k=5\n",
      "0.516\n",
      "0.546\n",
      "0.562\n",
      "0.58\n",
      "0.544\n",
      "evaluating k=8\n",
      "0.526\n",
      "0.576\n",
      "0.556\n",
      "0.57\n",
      "0.554\n",
      "evaluating k=10\n",
      "0.53\n",
      "0.592\n",
      "0.556\n",
      "0.568\n",
      "0.572\n",
      "evaluating k=12\n",
      "0.52\n",
      "0.588\n",
      "0.562\n",
      "0.564\n",
      "0.562\n",
      "evaluating k=15\n",
      "0.51\n",
      "0.58\n",
      "0.562\n",
      "0.562\n",
      "0.552\n",
      "evaluating k=20\n",
      "0.54\n",
      "0.562\n",
      "0.56\n",
      "0.564\n",
      "0.568\n",
      "evaluating k=50\n",
      "0.542\n",
      "0.576\n",
      "0.556\n",
      "0.538\n",
      "0.532\n",
      "evaluating k=100\n",
      "0.512\n",
      "0.54\n",
      "0.526\n",
      "0.512\n",
      "0.526\n",
      "k = 1, accuracy = 0.526000\n",
      "k = 1, accuracy = 0.514000\n",
      "k = 1, accuracy = 0.528000\n",
      "k = 1, accuracy = 0.556000\n",
      "k = 1, accuracy = 0.532000\n",
      "k = 3, accuracy = 0.482000\n",
      "k = 3, accuracy = 0.498000\n",
      "k = 3, accuracy = 0.486000\n",
      "k = 3, accuracy = 0.546000\n",
      "k = 3, accuracy = 0.528000\n",
      "k = 5, accuracy = 0.516000\n",
      "k = 5, accuracy = 0.546000\n",
      "k = 5, accuracy = 0.562000\n",
      "k = 5, accuracy = 0.580000\n",
      "k = 5, accuracy = 0.544000\n",
      "k = 8, accuracy = 0.526000\n",
      "k = 8, accuracy = 0.576000\n",
      "k = 8, accuracy = 0.556000\n",
      "k = 8, accuracy = 0.570000\n",
      "k = 8, accuracy = 0.554000\n",
      "k = 10, accuracy = 0.530000\n",
      "k = 10, accuracy = 0.592000\n",
      "k = 10, accuracy = 0.556000\n",
      "k = 10, accuracy = 0.568000\n",
      "k = 10, accuracy = 0.572000\n",
      "k = 12, accuracy = 0.520000\n",
      "k = 12, accuracy = 0.588000\n",
      "k = 12, accuracy = 0.562000\n",
      "k = 12, accuracy = 0.564000\n",
      "k = 12, accuracy = 0.562000\n",
      "k = 15, accuracy = 0.510000\n",
      "k = 15, accuracy = 0.580000\n",
      "k = 15, accuracy = 0.562000\n",
      "k = 15, accuracy = 0.562000\n",
      "k = 15, accuracy = 0.552000\n",
      "k = 20, accuracy = 0.540000\n",
      "k = 20, accuracy = 0.562000\n",
      "k = 20, accuracy = 0.560000\n",
      "k = 20, accuracy = 0.564000\n",
      "k = 20, accuracy = 0.568000\n",
      "k = 50, accuracy = 0.542000\n",
      "k = 50, accuracy = 0.576000\n",
      "k = 50, accuracy = 0.556000\n",
      "k = 50, accuracy = 0.538000\n",
      "k = 50, accuracy = 0.532000\n",
      "k = 100, accuracy = 0.512000\n",
      "k = 100, accuracy = 0.540000\n",
      "k = 100, accuracy = 0.526000\n",
      "k = 100, accuracy = 0.512000\n",
      "k = 100, accuracy = 0.526000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.array_split(X_train, num_folds)\n",
    "y_train_folds = np.array_split(y_train, num_folds)\n",
    "\n",
    "# print y_train_folds\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "################################################################################\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "\n",
    "for k in k_choices:\n",
    "    k_to_accuracies[k] = []\n",
    "\n",
    "for k in k_choices:\n",
    "    print 'evaluating k=%d' % k\n",
    "    for j in range(num_folds):\n",
    "        X_train_cv = np.vstack(X_train_folds[0:j]+X_train_folds[j+1:])\n",
    "        X_test_cv = X_train_folds[j]\n",
    "        \n",
    "        #print len(y_train_folds), y_train_folds[0].shape\n",
    "        \n",
    "        y_train_cv = np.hstack(y_train_folds[0:j]+y_train_folds[j+1:])\n",
    "        y_test_cv = y_train_folds[j]\n",
    "        \n",
    "        #print 'Training data shape: ', X_train_cv.shape\n",
    "        #print 'Training labels shape: ', y_train_cv.shape\n",
    "        #print 'Test data shape: ', X_test_cv.shape\n",
    "        #print 'Test labels shape: ', y_test_cv.shape\n",
    "\n",
    "        classifier.train(X_train_cv, y_train_cv)\n",
    "        dists_cv = classifier.compute_distances_no_loops(X_test_cv)\n",
    "        #print 'predicting now'\n",
    "        y_test_pred = classifier.predict_labels(dists_cv, k)\n",
    "        num_correct = np.sum(y_test_pred == y_test_cv)\n",
    "        accuracy = float(num_correct) / num_test\n",
    "        print(accuracy)\n",
    "        k_to_accuracies[k].append(accuracy)\n",
    "\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print 'k = %d, accuracy = %f' % (k, accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jkyRNTs4e1bgZj9mc4zm7dKzHD2bN+IruXVsLJUm1cPaoJpVWn1368Y//L729Syjim0Nv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\naoChTZIkqQH/By1bnMxV+ZiaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9a13d1a190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "  accuracies = k_to_accuracies[k]\n",
    "  plt.scatter([k] * len(accuracies), accuracies)\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Got 141 / 500 correct => accuracy: 0.282000\n"
     ]
    }
   ],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 7\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print 'Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy)"
   ]
  }
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